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Background
Radiologists with different experience levels and institutions use variable terminology for similar-appearing nerve lesions and struggle to describe neuropathy findings, temporal changes, post-intervention related changes, and associated findings in a straightforward manner. A standardized terminology for the description of various neuromuscular conditions was needed to improve an efficient practice. This issue has been recently addressed by a novel MRI-based classification and structured reporting system for peripheral neuropathy (PN), named Neuropathy Score Reporting and Data System (NS-RADS), validated through a multi-institutional study that demonstrated excellent inter-reader agreement. This presentation aims to discuss the NS-RADS with representative MR imaging examples and to present a step-by-step tutorial and algorithm on how to approach the scoring system to allow readers gain knowledge and apply it in their daily practice.

Educational Goals / Teaching Points
PN is a common condition, and may present with sensory or motor symptoms, including pain, weakness, dysesthesia, numbness, and paresthesia, with increased all-cause mortality rates. There are numerous causes of PN, ranging from congenital and hereditary to varied acquired etiologies. These conditions can be divided into different patterns of clinical presentation: predominantly motor, predominantly sensory, mixed motor-sensory conditions, congenital and familial, toxic metabolic syndromes, and autonomic neuropathy conditions including diabetes mellitus (DM) and amyloidosis. PN can also be classified as mononeuropathy or polyneuropathy, or based on their temporal evolution as acute or chronic. NS-RADS classes comprise nerve injuries, neoplasia, entrapment, diffuse neuropathy, post-interventional, as well as MRI study quality and muscles changes.

Key Anatomic/Physiologic Issues and Imaging Findings/Techniques
MRI readers can perform standardized PN assessments, report studies uniformly, and improve interdisciplinary communication for timely and optimized patient care and research. A summary with a proposed algorithm and a step-by-step on how to approach MRI cases and report using NS-RADS classes and subclasses will be presented, as follows: Step #1: MR study: N-0 and N-U. Step #2: nerve: injury subclass (I-1 to I-5), neoplasia subclass (N-1 to N-4), entrapment subclass (E-1 to E-3), diffuse neuropathy subclass (D-1 and D-2) and post-intervention (PI-1 to PI-3), and Step #3: muscle: M-0 to M-3.

Conclusion
NS-RADS brings solutions for problems related to heterogeneity in descriptions, impressions, and miscommunications regarding follow-up and management of patients. In the light of clinical history and examination findings, it can serve as a solid system for reporting, interdisciplinary communications, and futuristic longitudinal data collection for patient management and research.